A single list of the "top 10 most cited AI papers" does not exist, because the count for one paper changes with the database doing the counting. The closest independent ranking is a Nature analysis published on April 15, 2025, which listed the 25 most-cited papers of the 21st century across five citation databases; at least four landmark AI papers appear in it, deep residual networks at first place, the Transformer at seventh, and the AlexNet image classifier at eighth, with a deep learning review by LeCun, Bengio, and Hinton at 16th.
Key takeaways
- Nature's April 2025 ranking covered papers published since 2000 and drew on five databases, so each position summarizes several counts. As of October 2026 it remains the latest edition found; no update was located.
- The paper in first place, "Deep Residual Learning for Image Recognition," solved a training problem for very deep networks.
- A high citation count shows that a method became a standard tool. It does not show that the paper is the most correct or the most important in the field.
- Citation counts are higher in large fields, and methods papers are cited more than discoveries.
What Nature counted and how
The analysis, written by Helen Pearson, Heidi Ledford, Matthew Hutson, and Richard Van Noorden, appeared in Nature volume 640 on pages 588 to 592. According to the University of Toronto's summary, it measured academic citations across five major databases and covered tens of millions of papers published since 2000. The linked supplementary file is titled "Methodology, median rankings and full data for the most-cited papers of the twenty-first century," which points to an approach that compares median rankings across databases rather than relying on one database's number.
A companion Nature piece on the most-cited papers of all time used a single source, Web of Science, which it describes as holding 98 million papers back to 1900. Its top entry is a 1951 protein-measurement paper cited more than 350,000 times. The two lists answer different questions, and neither is a ranking of AI work specifically. AI papers appear in them because computer science became a very large citing community.
Nature has not published a new edition of the 21st-century list as of October 2026, so the April 2025 positions are the newest independent ones. Citation totals keep growing every day, which means the order of papers near each other can shift in any single database even while the Nature positions stand as published.
The AI papers in the ranking
The table lists the four AI papers whose Nature positions are confirmed by the University of Toronto's summary and by Nature's reporting. The descriptions come from each paper's own abstract or publisher page.
| Nature rank | Paper and year | Venue | What it contributed |
|---|---|---|---|
| 1 | He, Zhang, Ren, Sun, "Deep Residual Learning for Image Recognition," posted December 10, 2015 | IEEE CVPR 2016, pages 770 to 778 | Layers learn a correction to their input, which made networks of 152 layers trainable |
| 7 | Vaswani and seven coauthors, "Attention Is All You Need," posted June 12, 2017 | arXiv preprint (the arXiv page lists no venue) | The Transformer, a network built on attention alone, without recurrence or convolution |
| 8 | Krizhevsky, Sutskever, Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," 2012 | NIPS 2012 | A GPU-trained convolutional network that sharply cut error on ImageNet |
| 16 | LeCun, Bengio, Hinton, "Deep learning," May 27, 2015 | Nature 521, 436 to 444 | A review of how multi-layer models learn representations with backpropagation |
Why the residual paper leads
The residual learning paper reports networks up to 152 layers deep on ImageNet, eight times deeper than the VGG networks it compared against, with lower complexity. An ensemble of these networks won first place in the 2015 ImageNet classification task, and the authors also trained networks of 100 and 1,000 layers on the CIFAR-10 dataset. The idea is simple enough to reuse: each block adds its input back to its output, so a block that has nothing useful to add can leave the signal unchanged. That simplicity helped the paper become a building block inside later models for vision, speech, and language.
Medical imaging teams and satellite analysts both reuse the same residual design, and each reuse can add a citation, so the count reflects reach as much as depth.
Why the same paper has different counts
Databases differ in what they index. Some count only journals and selected conference proceedings, others include preprints, patents, theses, and reports. A paper that circulated as an arXiv preprint before a conference version appeared can be counted once, twice, or in split records depending on matching rules. A university alumni page about another paper in the Nature list gave its count as a range of 100,000 to 230,000, attributing the spread to multiple databases that track academic citations. Nature's median-rank approach is a response to that spread.
The worked example below uses invented numbers (illustrative example) to show why ranking by median position is steadier than ranking by one database's count.
| Paper (invented) | Rank in database A | Rank in database B | Rank in database C | Median rank |
|---|---|---|---|---|
| Paper X | 1 | 3 | 2 | 2 |
| Paper Y | 2 | 1 | 4 | 2 |
| Paper Z | 5 | 2 | 1 | 2 |
In this made-up case each database would crown a different paper, while the median shows the three as close neighbors. Real rankings need a tie-break rule, and Nature's supplementary file is the place where its choices are documented.
What a citation count cannot tell a reader
A citation records that a later paper pointed to an earlier one. It does not record whether the pointer agreed, disagreed, or was routine. Several limits follow.
- Field size matters: a large community produces more citations per paper than a small one, so a count compares poorly across fields.
- Tools outrank findings: software, statistical methods, and reporting guidelines are cited every time they are used. Nature's introduction notes that breakthroughs such as mRNA vaccines, CRISPR gene editing, the Higgs boson, and gravitational-wave measurements are absent from its top 25.
- Review papers collect citations as general references, which is a plausible reason a survey such as the 2015 deep learning review ranks high without introducing a new experiment.
- Recent papers have had little time to accumulate citations, so a ranking of the 21st century favors work from the 2010s.
- Counts differ by source, so a figure quoted without its database and date cannot be checked.
Readers who want the "most important" AI papers should look at expert surveys, prize citations, or course syllabi. These are different kinds of evidence from a citation total.
Checking a citation claim
- Which database produced the number, and on what date?
- Does the count merge the preprint and the conference or journal version?
- Is the paper compared with others in the same field and year range?
- Does the list say how ties and database disagreements were handled?
- Is the page quoting the number from the database itself or from another site that copied it?
FAQ
Is "Attention Is All You Need" the most cited AI paper?
Not in Nature's 2025 analysis. It placed seventh overall, behind the 2016 residual learning paper at first place. Other rankings built on one database can order the same papers differently.
Where can the current citation count of a paper be checked?
Web of Science, Scopus, OpenAlex, and Dimensions each publish counts for individual papers, and the figures will not match. The database name and the date should be recorded with any number.
Does a high count mean the paper is correct?
No. A count shows that other authors referred to the paper, which can happen to build on it, to criticize it, or to cite a standard reference.
Why are AI papers appearing in lists of all science?
Computer science grew into a very large publishing community, and neural network methods are reused across many other fields. Each reuse can add a citation to the original method paper.
Related reading
- Artificial intelligence and human reasoning
- Other names used for artificial intelligence
- Open challenges in artificial intelligence
- Optimization problems in AI
More in AI Basics.
Last verified: October 2026
Sources
- Exclusive: the most-cited papers of the twenty-first century, Nature 640, 588 to 592, Pearson, Ledford, Hutson, and Van Noorden, April 15, 2025
- These are the most-cited research papers of all time, Nature, April 2025
- Four papers authored by U of T scholars among the 25 most cited of the 21st century, University of Toronto News, July 9, 2025
- Deep Residual Learning for Image Recognition, He, Zhang, Ren, and Sun, arXiv:1512.03385, 2015
- Attention Is All You Need, Vaswani et al., arXiv:1706.03762, 2017
- ImageNet Classification with Deep Convolutional Neural Networks, Krizhevsky, Sutskever, and Hinton, NIPS 2012
- Deep learning, LeCun, Bengio, and Hinton, Nature 521, 436 to 444, 2015